This invention relates in general to a method of controlling electrochemical processes through use of multiple neural networks trained in prediction and pattern recognition techniques. One particularly useful application of the invention is to use two neural networks to control, for example, electrolytic cells used in the production of liquid aluminum.
For a number of processes, efficient control strategies are not used because the real-time measurement of certain crucial variables is either costly or difficult. In such instances, the variables may be estimated using virtual sensors around which different control strategies are developed. These sensors, and the resulting control strategies, can be developed based on neural networks. One example of such a control strategy development includes estimating the hardness variable (a major, non-measurable disturbance) in the grinding process of a mineral using a neural network. Other examples include: using neural networks to estimate the composition of a distilling column, the dissolution index in a polymer reactor and the biomass concentration in a fermentation system; using neural models to predict pH and fermenting time of a biologically active culture; and controlling a bio-reactor by using neural network models to estimate the biomass concentration from the continuous measurement of the flow of carbon dioxide combined with the dilution rate of the reactor.
Neural networks are computer programs that emulate the way the human brain processes information. Neural networks may be defined as computing systems which are made of a number of simple, highly connected processing elements, which processes information by its dynamic state response to external inputs. More specifically, a neural network is a network of several simple processors (xe2x80x9cunitsxe2x80x9d or xe2x80x9cneuronsxe2x80x9d), each independent of the other, possibly equipped with local memory and connected by unidirectional transmission channels (xe2x80x9cweightsxe2x80x9d or xe2x80x9cconnectionsxe2x80x9d). These units operate in parallel on their local digital data and on the data received via the connections. The basic processing element, called an xe2x80x9cartificial neuron,xe2x80x9d is modeled to mimic the characteristics of a biological neuron. Neural networks may be generally characterized by the following components:
A group of units for processing.
An activation function or a transfer function, for each unit.
A network architecture or topology. This is the way in which the units are laid out and connected one to the other.
A propagation rule by which the units"" activities are propagated in the network.
An activation rule which allows the activity of each neuron to be updated.
An outside environment with which the network interacts.
A learning rule for updating the connections.
A single neuron can only perform elementary operations, but several neurons working together and organized as one or more layers can take up much more complex information processing tasks. Although all neural networks are constructed using artificial neurons as building blocks, neural networks can differ greatly in architecture and in learning rules. Neural network architecture includes elements such as the number of layers, the number of neurons in each layer, the shape of the activation function, and the way the layers are interconnected. The term xe2x80x9clearning rulexe2x80x9d refers to the process through which the network acquires the necessary knowledge by adapting the weights of its connections.
Liquid aluminum is produced by dissolving alumina (Al2O3) reactant in a molten cryolite (Na3AlF6) bath, and decomposing it electrolytically to obtain liquid aluminum. A high-intensity, low-voltage, constant electric current passes through the electrolytic cell from the carbon anode to the bath, then on to the carbon cathode. The carbon cathode is built in the form of a receptacle to facilitate the gathering of the liquid aluminum produced. The oxygen freed by the electrolysis is drawn to the anode, and the anode is gradually consumed to produce carbon dioxide (CO2). The consumable anode is a typical feature of the process.
In addition to cryolite, the bath usually contains various additives, mainly aluminum fluoride (AlF3) and calcium fluoride (CaF2), the purposes of which are to improve the physico-chemical properties of the bath and to lower its melting temperature.
A good control of the cell is required in order to maintain its operation close to the targeted main process variables. The most important of these variables are the cell resistance and the alumina concentration in the bath. The two are related through a characteristic curve giving cell resistance as a function of concentration. Depending on the condition (also called the xe2x80x9cstatexe2x80x9d) of the cell, this the shape of the characteristic curve may vary. The condition of a cell is determined by a number of elements describing the operation of the cell, such as, for example, the thermal condition, present alumina concentration, and the stability of the cell. Because the relative importance of each of these elements cannot be weighed independently, the combination of elements making up the condition of the cell may be described alternately herein as the xe2x80x9ccell conditionxe2x80x9d or xe2x80x9ccell state.xe2x80x9d
To be efficient, the control of alumina feeding of the electrolytic cell must be based on cell resistance, alumina concentration and cell state. A too high concentration of alumina may lead to the formation of xe2x80x9csludge,xe2x80x9d an undissolved slurry that is difficult to remove and causes inefficiencies in the current distribution in the cathode, thus disturbing cell operation. A too low concentration of alumina may trigger an xe2x80x9canode effect,xe2x80x9d an undesirable event characterized by a rapid buildup of the gas layer below the anode-bath interface. Anode effects increase cell resistance, causing cell voltage to increase rapidly. Anode effects can cause high power consumption, high bath temperature, production of carbon monoxide (CO) and carbon tetrafluoride (CF4). The high bath temperature can cause a partial melting of the xe2x80x9cfreezexe2x80x9d (that outer part of the bath that solidifies along the cell walls, to help protect the cell walls against the highly corrosive cryolite) and consequently destabilize the thermal balance of the cell.
A stable energy balance helps to stabilize the bath temperature and freeze formation. If unstable, the freeze may melt or grow, both undesirable conditions. A good material balance helps keep the alumina concentrations at or near the optimal values.
The ideal operating point of the cell is where the alumina concentration and cell resistance are low. However, as alumina concentration is decreased, cell resistance increases rapidly. In order to avoid anode effects and their accompanying unfavorable conditions, it is of tantamount importance to carefully control both the cell resistance and the alumina concentration.
Operators usually control their cells through a controller that incorporates in coded form the theoretical and experimental knowledge of the process into a combination of software and hardware. Generally, the controller takes the cell current and cell voltage and generates the cell resistance, in addition giving information about the time rate of change of the cell resistance. From those decision variables, the controller takes actions to modify the anode position by adjusting the anode-cathode distance, or to change the alumina feed rate by varying the feeding frequency and the duration of the feeding periods.
Prior art non-neuron or xe2x80x9cstandardxe2x80x9d control logics work by modifying the anode-cathode distance and adjusting the alumina feedrate frequency and duration However, various time lags are inherent in the process, such as the delay caused by the time required to dissolve the alumina in the bath. Due to such time lags, this control logic often cannot act in time to prevent the anode effects. In addition, the decision criteria of the standard control logic are fixed criteria, as such cannot be tied explicitly to alumina concentration or the cell condition.
These standard control schemes now in use are based only on cell resistance, and thus constitute an open-loop control means that lacks robustness because its decision criteria are not explicitly tied to cell state or alumina concentration. Under a open-loop control structure, in the presence of large disturbances, the cell drifts away from its optimal operating values and operates at either too low or too high alumina concentrations, potentially leading to anode effects or sludge formation, respectively. This results in non-optimal cell control, and decreased efficiency.
In addition, electrochemical cells experience amperage fluctuations around the nominal value, which can disturb the cell process and decrease cell performance. It is desirable to control the cell to minimize the effects of these perturbations, such as an increase in anode effect frequency, disturbances of heat and mass balances, and cell instability, among other deleterious effects. If, however, the control logic is efficient and the operating parameters of the cell (such as bath and metal heights, anode-cathode distance, base resistance as set point, chemistry, etc.) are correct, the cell will operate smoothly, and deleterious effects will be minimized. Efficient cell control allows the cell to operate more smoothly and at higher amperages, thus producing more metal.
Thus, it would be advantageous to improve standard control logic by using neural networks and predictive technique to predict future values of cell resistance and to identify the real-time condition of the cell, thus conferring to the control logic a predictive capability. It would also be advantageous to minimize the anode effects in electrolytic cells by taking appropriate feeding actions early enough in view of the various time lags, such as the delay caused by the time required to dissolve the alumina inside the bath. It would be further advantageous to have a means of preventing destabilizing events such as anode effects and sludge formation through control logic. It would also be advantageous to improve standard control logic by adding pattern-recognition control based on the identification of the condition of the cell and the estimation of the alumina concentration, thus conferring to the cell a closed-loop control structure which allows the cell to operate at a selected alumina concentration independent of its state. It would also be useful to improve standard control by adding predictive control based on cell resistance. Lastly, it would also be useful to use the condition of the cell and estimation of alumina concentration to provide a reliable deduction of the alumina concentration in the cell.
In this invention, a neural control logic scheme, utilizing both prediction and pattern-recognition networks, has been devised and applied to the control of electrochemical cells, such as aluminum electrolytic cells. Efficient cell control requires the knowledge of predicted values of the decision variables in order to enable the cell controller to take anticipated actions to minimize destabilizing anode effects during cell operation. Efficient cell control also requires knowledge of the reactant (such as alumina) concentration in order to adapt the decision-making criteria of the cell such that the cell operates at a near-optimal regime independently of the condition of the cell.
This invention, in contrast to prior art conventional control, provides an intelligent efficient control scheme utilizing both predictive and pattern-recognition methods through multiple neural networks which operate to reduce deleterious effects in the cell and optimize the efficiency of the cell. As defined herein, xe2x80x9cconventionalxe2x80x9d control shall mean non-neural control and control using a single algorithm and single neural network.
In the preferred embodiment (an aluminum electrochemical cell), the predictive capacity of a feedforward neural network is used to predict cell resistance and its rate of change over time, which is then applied to the control logic of the cell. The predicted values are used to generate anticipated control actions to be applied to the cell at different cell states, in order to avoid the anode effects induced by either reduced amounts of alumina injected by dump or reduced feeding frequency and duration.
Performances of the standard control logic and the neuro-predictive control logic were compared through results obtained from computer simulations, showing the efficiency of predictive control logic in suppressing anode effects as compared to standard methods of cell control. As a consequence, thermal stability is increased, power consumption is decreased and cell life is lengthened. Avoiding or reducing anode effects also results in reduction of harmful emissions such as fluorocarbon gases.
Next, the pattern recognition capacity of an LVQ neural network was used to identify the present condition, or state, of the cell on a real-time basis through the resistance versus concentration curve of the cell, from which the alumina concentration in the cell is deduced. The typical conditions of a cell were identified and each was associated with a set of triggers, collectively referred to herein as a xe2x80x9ccodebook.xe2x80x9d In contrast to convention control models, the neural logic of the invention utilizes one set of triggers for each typical cell condition, while conventional control utilizes a single set of triggers for all cell conditions. The selected triggers used for control in the method of the invention depends upon the real-time condition of the cell.
In the preferred embodiment of the invention (an aluminum reduction cell), the decision criteria for the feeding control logic are adapted to optimize alumina concentration, thus giving rise to a closed-loop cell control structure which enables the cell to operate at a near-optimal alumina concentration independent of the condition of the cell. This operation minimizes anode and other undesirable effects, allowing a more efficient control and better cell stability, which in turn help to increase the amperage and productivity of the cell.
The predictive and pattern-recognition techniques of the invention utilize neural network construction and training using past and present data specific to the process. After training and validation, these processors are then integrated with the cell control logic, which utilizes dual neural networks for a continuous and efficient process control logic which is more effective than conventional control methods.